Papers by Akshay Batheja

2 papers
Improving Machine Translation with Phrase Pair Injection and Corpus Filtering (2022.emnlp-main)

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Challenge: In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation systems.
Approach: They propose to combine Phrase Pair Injection and Corpus Filtering to boost performance of Neural Machine Translation systems.
Outcome: The proposed method improves machine translation models on low-resource language pairs . BLEU score improves over models trained with whole pseudo-parallel corpus augmented with parallel corpus.
“A Little is Enough”: Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation (2023.findings-acl)

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Challenge: Quality Estimation (QE) is the task of evaluating the quality of a translation when reference translation is unavailable.
Approach: They propose a Quality Estimation based Filtering approach to extract high-quality parallel data from the pseudo-parallel corpus.
Outcome: The proposed approach improves the machine translation system performance by up to 1.8 BLEU points over the baseline model.

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